{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Derivatives Analytics with Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Appendix &mdash; Python in a Nutshell**\n",
    "\n",
    "**Wiley Finance (2015)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/images/derivatives_analytics_front.jpg\" alt=\"Derivatives Analytics with Python\" width=\"30%\" align=\"left\" border=\"0\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yves Hilpisch\n",
    "\n",
    "The Python Quants GmbH\n",
    "\n",
    "<a href='mailto:analytics@pythonquants.com'>analytics@pythonquants.com</a>\n",
    "\n",
    "<a href='http://pythonquants.com'>www.pythonquants.com</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python Fundamentals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First Steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3 + 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3 / 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.75"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3 / 4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "a = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# sin(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from math import sin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1411200080598672"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sin(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "b = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.7568024953079282"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.sin(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def f(x):\n",
    "    return x ** 3 + x ** 2 - 2 + math.sin(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.909297426825681"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34.141120008059865"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f(a) = 34.141\n",
      "f(b) = 77.243\n"
     ]
    }
   ],
   "source": [
    "%run A_pyt/a_first_program.py "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Array Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "a = np.arange(0.0, 20.0, 1.0)  # (start, end, step)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,\n",
       "        11.,  12.,  13.,  14.,  15.,  16.,  17.,  18.,  19.])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "a.resize((4, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0.,   1.,   2.,   3.,   4.],\n",
       "       [  5.,   6.,   7.,   8.,   9.],\n",
       "       [ 10.,  11.,  12.,  13.,  14.],\n",
       "       [ 15.,  16.,  17.,  18.,  19.]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  1.,  2.,  3.,  4.])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0]  # first row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 15.,  16.,  17.,  18.,  19.])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[3]  # fourth (=last) row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[1, 4]  # second row, 5th (=last) element"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7.,  8.])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[1, 2:4]  # second row, third & forth element"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0. ,  0.5,  1. ,  1.5,  2. ],\n",
       "       [ 2.5,  3. ,  3.5,  4. ,  4.5],\n",
       "       [ 5. ,  5.5,  6. ,  6.5,  7. ],\n",
       "       [ 7.5,  8. ,  8.5,  9. ,  9.5]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a * 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   0.,    1.,    4.,    9.,   16.],\n",
       "       [  25.,   36.,   49.,   64.,   81.],\n",
       "       [ 100.,  121.,  144.,  169.,  196.],\n",
       "       [ 225.,  256.,  289.,  324.,  361.]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0.,   2.,   4.,   6.,   8.],\n",
       "       [ 10.,  12.,  14.,  16.,  18.],\n",
       "       [ 20.,  22.,  24.,  26.,  28.],\n",
       "       [ 30.,  32.,  34.,  36.,  38.]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a + a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def f(x):\n",
    "    return x ** 3 + x ** 2 - 2 + np.sin(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ -2.00000000e+00,   8.41470985e-01,   1.09092974e+01,\n",
       "          3.41411200e+01,   7.72431975e+01],\n",
       "       [  1.47041076e+02,   2.49720585e+02,   3.90656987e+02,\n",
       "          5.74989358e+02,   8.08412118e+02],\n",
       "       [  1.09745598e+03,   1.44900001e+03,   1.86946343e+03,\n",
       "          2.36442017e+03,   2.93899061e+03],\n",
       "       [  3.59865029e+03,   4.34971210e+03,   5.19903860e+03,\n",
       "          6.15324901e+03,   7.21814988e+03]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "for i in xrange(5):\n",
    "    print i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "b = np.arange(0.0, 100.0, 1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50.0 at index no. 50\n"
     ]
    }
   ],
   "source": [
    "for i in range(100):\n",
    "    if b[i] == 50.0:\n",
    "        print \"50.0 at index no. %d\" % i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000 divided by 17 gives 58.824\n"
     ]
    }
   ],
   "source": [
    "print \"%d divided by %d gives %6.3f\" % (1000, 17, 1000./17)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "np.random.seed(5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "b = np.random.standard_normal((4, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.64371681, -1.25182043, -0.56391455,  0.3314386 ,  1.20390744],\n",
       "       [ 0.41091404,  1.67824248, -1.02596417, -0.02176213,  0.53048021],\n",
       "       [ 0.57600497, -1.55430075,  0.13509601, -0.6231574 ,  1.42761494],\n",
       "       [-2.45615932, -1.62936212,  1.66033378, -0.30442536, -0.55443482]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2.6749854009795335"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.13374927004897669"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.1148394461082733"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['font.family'] = 'serif'\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rrlBVleLYYwt7/bffrqRPH5g0qenXt2iR4p57YOTISrp3h3PPTdGrV3G0XyqV\noqqqCuCb78tMxTKhzMw6AbcDF4cQFprZkSGE4fVek9WEssZcdhm8+KLfBq6zTmSnTctFF/k1b7ih\nsNcVKRX/+Q9UVvq6Q5bRVKjcHHYYHHusL02TrmeegTPO8C1mb7rJ7xqKSSlNKBsL7Ag8amYTgbyP\nrL/2WmjbFi6+ON9XWtuqVd5XEfeSErW/ICQaas9oLViQYt11fU5BoaxcCZMmwYEHZva+H/3It9n8\n4gsvaT3/fH7iK6S8lYaaEkLoVehrtmzp44R33RV69fI1iQph7Fj4/vdhm20Kcz2RUmTmw0jHjvWF\n6Arh5Zdhq61g440zf2/Hjt5v8OSTPufguOPguuv8x2YpKssJZY3p2NE7jy+4wNc3KYS//jX+uwHQ\nuPeoqT2jVVlZWfBhpOPGNTxaKBOHHeaL5s2fD7vs4vuPl6JEJQLwXxv33gtHHEFeh6stWeJLSfzr\nX16DFJGmHXCAbzf71VeFuV79+QPZ6tQJ/v537zc47DBYujT3cxZa4hIB+KSuY4+FY47xGn7Upk/3\n8lPr1vDKK/HNHahLNe1oqT2jlUql6NABevaEyZPzf73Fi73OH+UKxRdcALvv7hWA6urozlsIiUwE\n4PW8Vq3gkkuiO2cIPua4Tx+45hp44IHiG1EgUswKVR6aONEnsrVpE905zeCee2DRIrj++ujOWwhl\ntx9BJv7v/7zz+LrrMhs+1ti5Tj3Vh78NHeqLy4lIZl55xf8evfVWfq9z9tnQtWt+lqBZtAh22w3u\nvttLRYVWSsNHi8KGG/qiUuedBzNmZH+e55+HnXf2EQgvvqgkIJKtXr38i3TBgvxep7FlJaKw2WYw\nfLjPQi7kcNhcJDoRgK8weNddcPjhvjBcJlav9lvAo47yW8LbbvN+gWKkmna01J7Rqm3Pli3hoIP8\nizpf3n/fO3R32il/19htN9/s5rDDvFpQ7BKfCMA7jms7kNPtPF60yMc9P/ssvPoqDBjQ/HtEpHm1\n8wnypXbYaL5nMJ988pqZy/kYlBKlRPcR1LVqlc8Y3GUXnzbelLFj4ZRT4Mwz4Yor/FeMiERjwQKf\nsfvxx/n5u3XUUb5veCF2Cly1Cvr188rDrbfm/3qgPoKctGrlnbx//zsMG9bwa1auhN/8Bk47zbec\nvPpqJQGRqG2xhdfZ87Fi8OrV8Nxz+esfqK/2e2XkSHj44cJcMxtKBHV85zveeXzOOT5bsK65c30X\no7fe8nkJufDNAAAKy0lEQVQCpTaxVDXtaKk9o1W/PfM1jPTVV+G73/VEUygbbuhLUfzqVzBtWuGu\nmwklgnp69oQ77vDO49pOnuHDvfPn6KNh1CjYaKN4YxQpd7X9b1GLYlmJbOywA9x/v69o8OGHhb9+\nc9RH0IgLL/Rf/1tt5R/IoUN9zoGI5N9XX/licAsWRDszf9994dJL4ZAmd0vJn2uu8e+T556DddfN\nzzXURxChm27y7S4XL/bNrJUERAqnbVvYc0//wozKkiVe1t133+jOmakrroBNNoFf/MJXIigWSgSN\naNXKN6AYMqQ41grKlWra0VJ7Rquh9oy6PDRpkv+gW2+96M6ZqRYtfA/1l1/2xS+LhRKBiBSl2vkE\nUf1yfvbZaFYbzVX79t55fO21UCy/J9RHICJFKQQfSppKQbduuZ9vu+18t8BeBd8Wq2ETJsAJJ8DU\nqZDlVsMNUh+BiJQNs+iGkc6f7/uP7Lxz7ueKyoEH+urHAwfCl1/GG4sSQUKoph0ttWe0GmvPqPoJ\nxo3zL94WRfaN98tf+pD1U06Jt/O4yJpFRGSNgw7yTt4VK3I7T1zzB5pjBvfdB/PmwY03xhhHsdbh\n1UcgIuAjfW65JfvZ/NXVPmTztdfge9+LNLTILFzok1YHD4ZDD83tXOojEJGyk2t5aMYMXz6mWJMA\nwOabwxNPwPnnw/Llhb++EkFCqKYdLbVntJpqz1yXpS7WslB9e+wBM2dGu31mupQIRKSo7bEHvPee\nL0udjWKZP5COdu3iua76CESk6A0c6Is+Hn98Zu9btsz7BxYu9IlcSaA+AhEpS9mWh6ZM8bkDSUkC\n2VIiSAjVtKOl9oxWc+1Z22GcaZEgn5vUlxMlAhEpelttBeuvD2++mdn7SqWjOG7qIxCRkvCLX/ia\nPBddlN7rFy3yDWE+/thXE06KkukjMLNfmtn9ZnaRmY00sz3iiENESkem/QTjx8P++ycrCWQrrtJQ\na+CcEMItQBVwbUxxJIZq2tFSe0YrnfasrPR1/NNdoG3cuNIZNhq3WBJBCOGWEMLXNYfdgLfjiENE\nSscGG8Auu8Dkyc2/NgT1D2Qib30EZjYG2KSBp64IIYw2s02AS4GewBEhhM/qvV99BCKylhtu8Jr/\n7bc3/bo33/S5B++9V5i4ikk2fQR5q56FEJrcHjqE8BHwSzPbH3ga2D1fsYhIeejbF048sfnXldJs\n4mIQSzeKmf06hHBrzeFcYKuGXjdo0CC61GzdU1FRQc+ePamsWYKwtqao4/SOb7/9drVfhMdqz2iP\n023Pffet5NNPYdiwFJts0vj5hg1LMWAAQHH89+XzOJVKUVVVBfDN92WmYhk+amZ3AiuAT4EewJAQ\nwj/rvUaloQilUqlvPkSSO7VntDJpz+OPhwMOgNNPb/j55ctho418V7KKiuhiLBXZlIY0j0BESspD\nD8Ho0fD44w0/P2ECXH45vPRSYeMqFiUzj0BEJFsHH+xf9qtXN/y8RgtlTokgIWprihINtWe0MmnP\nzTeHLbaAadMafl7zBzKnRCAiJaexWcaffAKzZ8PuGoOYEfURiEjJGTcOrroKXnxx7ceHDoXHHoN/\n/rPh9yWB+ghEJBH22QfeegsWL177cc0fyI4SQUKoph0ttWe0Mm3PNm1gr72807iWlpXInhKBiJSk\n+v0E//43mMEPfhBfTKVKfQQiUpJmzYJ+/WDOHE8Ad94Jb7wBDzwQd2TxUh+BiCTGdtv5XIJ33/Vj\nlYWyp0SQEKppR0vtGa1s2tNsTXloxQpfnvrAA6OPLQmUCESkZPXp4yOFpk6Fbt2gU6e4IypN6iMQ\nkZL1f//n+xj//OfQsiXceGPcEcVPfQQikigbbgjbbw/33qv5A7lQIkgI1bSjpfaMVi7t2aePzyHo\n3Tu6eJImlo1pRESicvTRvqH9uuvGHUnpUh+BiEgZUR+BiIhkTIkgIVTTjpbaM1pqz3gpEYiIJJz6\nCEREyoj6CEREJGNKBAmhGmy01J7RUnvGS4lARCTh1EcgIlJG1EcgIiIZUyJICNVgo6X2jJbaM15K\nBCIiCac+AhGRMqI+AhERyVhsicDMLjOzT+K6ftKoBhsttWe01J7xiiURmFkl0BFQ7adAZsyYEXcI\nZUXtGS21Z7wKngjMbBPgaOAuIKM6lmRv8eLFcYdQVtSe0VJ7xisvO5SZ2RhgkwaeuhI4DLgQvyMQ\nEZGY5SURhBAOaehxM+sFrAR+jieCtmZ2MfCPEMLsfMQibu7cuXGHUFbUntFSe8YrtuGjZtYFmBZC\n2KiR59V/ICKShUyHj8ayeb2ZdQXOAtqY2aXA7SGEZXVfk+l/iIiIZKdoJ5SJiEhhaEKZiEjCxVIa\nao6ZHQQcDnwMhBDCtTGHVNLMbCrwVc3hqhDCwXHGU0rMbFPgOqB7CGG3msc2BG4E3ge6AZeGED6O\nL8rS0Uh7Xg3sV+dl14cQxscQXsmpKbP/Dngd2AL4LITwu0w/o0WXCMxsPWAwsH0IYaWZPWFmB4QQ\nnos7thL2TAjhmriDKFF7ASOBHnUeuwEYF0J4wswGALcCJ8cRXAlqqD1DCGH/mOIpdR2BISGEUQBm\n9raZPQWcSQaf0WIsDe0JzAshrKw5fgHoH2M85WAnM7vYzK4ys35xB1NKQgjDgaX1Hu4HvFTz7y+i\nz2faGmlPzOxSM7uw5nPaNobQSlII4dXaJFCjBfAlGX5Gi+6OANgYWFLn+IuaxyR7N4UQpplZC2Cy\nmS0JIUyJO6gSVvcz+gXQ0cxahBCqY4yplD0OzAkhfGVmZ+GrDpwec0wlx8wOB8aEEP5tZhl9Rovx\njuAjoH2d4w41j0mWQgjTav5ZDUwBdBuem49Z8xndAPhcSSB7IYRZIYTaPqyJwAFxxlOKzGx/YL8Q\nwgU1D2X0GS3GRDAV2NLMWtcc9waeijGekmZm25jZqXUe6gZoFndunsI/l+A179ExxlLyzOzmOof6\nfGbIzPoDfUII55vZ5ma2Jxl+RotyHkHNqKGjgE+AFSGE38UcUskys82Au4Hp+C+DViGEX8UbVekw\ns33xTra++CCG24C2wE3APKAr8JsQgpZUT0Mj7XklsB7+K3Yn4AotOZOemmV7UsA0fBHP9fG/76PI\n4DNalIlAREQKpxhLQyIiUkBKBCIiCadEICKScEoEIiIJp0QgIpJwSgQiIgmnRCASMTO7xswOjTsO\nkXRpHoGISMLpjkASzcx+a2bLzGwfM/uVmT1lZt3qvWYPMxtiZheZ2aNm1tnMOpjZY2Y218w2MbN/\nmtndNc+NNLOrat57tpldb2aXmNngeP4rRZqmOwJJPDO7HF9RdBXw2xDC1/We3xFYGkKYW7PC454h\nhIvNzIAx+Pr6m4UQrqx5/U+BLUMI15rZdOC8EMIUM9szhPASIkWmGJehFim064FZwF31k0CN5cA5\nZvYp8H2gNfhuKmZ2Or4LVPd677Gafw4CLjazW4GHWbNGvEjRUGlIBI4Afg/8wsy+38DzNwMzQwi/\nB4bXe+40/Mv+r2a2Ts1jVuf574UQTsCXVr7QzCoijVwkArojkESrWaL7Z8Ch+Jf1CDM7t97GPY8A\n59Ykic74jm+7AccBW+IraP4GGGZmtwEDgAoz2w441Mx2BgIwPISwuFD/bSLpUh+BiEjCqTQkIpJw\nSgQiIgmnRCAiknBKBCIiCadEICKScEoEIiIJp0QgIpJwSgQiIgn3/5X3mJN58urbAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffd4496b150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(np.cumsum(b))\n",
    "plt.xlabel('x axis')\n",
    "plt.ylabel('y axis')\n",
    "plt.grid()\n",
    "plt.savefig('../images/A_pyt/line_plot.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XNe70i9PTefqzo//mvJr1i7p1eJZNpdIw9OOc1yryvPJiOZ5WJ5Xq9M3MbHZV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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffd448cbc90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c = np.resize(b, 20)\n",
    "plt.figure()\n",
    "plt.subplot(211)\n",
    "plt.plot(c, 'ro') # red dots\n",
    "plt.grid()\n",
    "plt.subplot(212)\n",
    "plt.bar(range(len(c)), c)\n",
    "plt.grid()\n",
    "plt.savefig('../images/A_pyt/dots_bars.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## European Option Pricing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Black-Scholes-Merton"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value of European call option is   15.655\n"
     ]
    }
   ],
   "source": [
    "%run A_pyt/b_BSM_valuation.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Binomial Option Pricing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value of European call option is   15.654\n",
      "CPU times: user 1.25 s, sys: 3 ms, total: 1.26 s\n",
      "Wall time: 1.25 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "%run A_pyt/d_CRR1979_loop.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value of European call option is   15.654\n",
      "CPU times: user 152 ms, sys: 4 ms, total: 156 ms\n",
      "Wall time: 157 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "%run A_pyt/e_CRR1979_vectorized.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value of European call option is   15.654\n",
      "CPU times: user 1e+03 µs, sys: 0 ns, total: 1e+03 µs\n",
      "Wall time: 1.45 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "%run A_pyt/f_CRR1979_fft.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Monte Carlo Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value of European call option is   15.603\n"
     ]
    },
    {
     "data": {
      "image/png": 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h5ZnqTFrHoTsSgwrlQCSdKOmzks6RtKnsePppag62TnmPvGbZAVjbsuMvf11J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\nD2iseStfaJsKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffd3cbb6e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%run A_pyt/g_MCS.py\n",
    "plt.savefig('../images/A_pyt/histogram.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Financial Topics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Approximation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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StZlHhtCYcyX/nXf0HcBp8HZNuuswT8Fv5x2ojczMTKlWrVqR9du2bevV8e/a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\nYKjjVxRFqWCo41cURalg/H8PwG1b2pRwKQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffd3aa941d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%run A_pyt/h_REG.py\n",
    "plt.savefig('../images/A_pyt/regression.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2dTqhqokePLWBV+pumtM+XsgI8efMzM5m7tq15BcVMXftWv7n8mzeeAO6doUB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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffd3ab219d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%run A_pyt/i_SPLINE.py\n",
    "plt.savefig('../images/A_pyt/splines.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: -1.000000\n",
      "         Iterations: 18\n",
      "         Function evaluations: 36\n",
      "Global Minimum is -1.571593\n",
      "Local Minimum is  -1.570801\n"
     ]
    }
   ],
   "source": [
    "%run A_pyt/j_OPT.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Integration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value of the integral is 1.718281828\n"
     ]
    }
   ],
   "source": [
    "%run A_pyt/k_INT.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Advanced Python Topics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Object Orientation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%run A_pyt/l_CLASS.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "o1 = Option(105., 100., 1.0, 0.05, 0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.654719726823579"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "o1.value()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# o1.vega()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "o2 = OptionVega(105., 100., 1.0, 0.05, 0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.654719726823579"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "o2.value()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "73.345040765170197"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "o2.vega()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Input Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<closed file 'option_container', mode 'w' at 0x7ffd44661300>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "o1 = Option(105., 100., 1.0, 0.05, 0.25)\n",
    "\n",
    "o2 = OptionVega(105., 100., 1.0, 0.05, 0.25)\n",
    "\n",
    "import cPickle as cp\n",
    "\n",
    "options = open('option_container', 'w')\n",
    "\n",
    "cp.dump(o1, options)\n",
    "\n",
    "cp.dump(o2, options)\n",
    "\n",
    "options.close()\n",
    "\n",
    "options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "options = open('option_container', 'r')\n",
    "option1 = cp.load(options)\n",
    "option2 = cp.load(options)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.654719726823579"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "option1.value()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "73.345040765170197"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "option2.vega()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "options.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "options = open('option_container_2', 'w')\n",
    "cp.dump((option1, option2), options)\n",
    "options.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<__main__.Option instance at 0x7ffd44625248>,\n",
       " <__main__.OptionVega instance at 0x7ffd44999e60>)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optionstore = open('option_container_2', 'r')\n",
    "olist = cp.load(optionstore)\n",
    "olist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(olist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<__main__.Option instance at 0x7ffd44625248>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "olist[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.654719726823579"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "olist[0].value()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "73.345040765170197"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "olist[1].vega()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Excel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               Open     High      Low    Close     Volume  Adj Close\n",
      "Date                                                                \n",
      "2014-11-17  9162.27  9331.32  9161.60  9306.35   72034400    9306.35\n",
      "2014-11-18  9323.75  9461.53  9323.52  9456.53   73982400    9456.53\n",
      "2014-11-19  9462.05  9521.73  9439.16  9472.80   73153500    9472.80\n",
      "2014-11-20  9460.40  9487.69  9382.23  9483.97   82097800    9483.97\n",
      "2014-11-21  9521.24  9736.14  9508.17  9732.55  166634400    9732.55\n",
      "2014-11-24  9722.31  9832.41  9711.77  9785.54   97612300    9785.54\n",
      "2014-11-25  9790.03  9921.46  9787.26  9861.21  117773900    9861.21\n",
      "2014-11-26  9894.60  9942.67  9868.35  9915.56   89124300    9915.56\n",
      "2014-11-27  9934.78  9992.67  9920.86  9974.87   84700200    9974.87\n",
      "2014-11-28  9990.70  9990.70  9902.40  9980.85   98906800    9980.85\n"
     ]
    },
    {
     "data": {
      "image/png": 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/EXlIRK4WkXtFpEkyz99Q+/5qIwxawqDBJyxa8kXH7NlW2UdOSItkm21gyy3N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\nhw7v/smURzsv/V7gTe/4HeDRdLQSki0PseGmY1X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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffd32466c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%run A_pyt/m_Excel_read.py\n",
    "plt.savefig('../images/A_pyt/DAX_history.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rapid Financial Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MCS call value estimate is  913.334\n"
     ]
    },
    {
     "data": {
      "image/png": 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H0JAn8uKLqfNrh/z6q/2fO9HHA7nUEKFDQq6zbJl1w+XlmVPX+vVm5GbOjAeu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\n3gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffd4496b390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%run A_pyt/n_pandas.py\n",
    "plt.savefig('../images/A_pyt/DAX_hist_rets.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 2535 entries, 2005-01-03 to 2014-11-28\n",
      "Data columns (total 7 columns):\n",
      "Open         2535 non-null float64\n",
      "High         2535 non-null float64\n",
      "Low          2535 non-null float64\n",
      "Close        2535 non-null float64\n",
      "Volume       2535 non-null int64\n",
      "Adj Close    2535 non-null float64\n",
      "Returns      2534 non-null float64\n",
      "dtypes: float64(6), int64(1)\n",
      "memory usage: 158.4 KB\n"
     ]
    }
   ],
   "source": [
    "DAX.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9980.8496090000008"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.22005937542095302"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  9980.849609  ,   9980.849609  ,   9980.849609  , ...,\n",
       "          9980.849609  ,   9980.849609  ,   9980.849609  ],\n",
       "       [  9780.11051858,   9596.76376197,   9804.42993781, ...,\n",
       "         10178.49298267,  10544.91227252,  10772.784671  ],\n",
       "       [  9917.52216091,   9424.01876152,  10133.98037603, ...,\n",
       "         10724.24489763,  10096.996348  ,  11039.81395101],\n",
       "       ..., \n",
       "       [  8781.42892863,   9981.67356521,   8910.13723828, ...,\n",
       "         10159.79403127,  15693.40215222,  14432.77708357],\n",
       "       [  8565.58206333,   9525.85935849,   9159.99353242, ...,\n",
       "          9841.83698368,  16076.59936925,  14482.47842989],\n",
       "       [  8696.2187666 ,   9214.70122711,   9141.32360409, ...,\n",
       "          9761.98117371,  16022.68770718,  14504.19589747]])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
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    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>\n",
    "\n",
    "<a href=\"http://www.pythonquants.com\" target=\"_blank\">www.pythonquants.com</a> | <a href=\"http://twitter.com/dyjh\" target=\"_blank\">@dyjh</a>\n",
    "\n",
    "<a href=\"mailto:analytics@pythonquants.com\">analytics@pythonquants.com</a>\n",
    "\n",
    "**Python Quant Platform** |\n",
    "<a href=\"http://quant-platform.com\">http://quant-platform.com</a>\n",
    "\n",
    "**Derivatives Analytics with Python** |\n",
    "<a href=\"http://www.derivatives-analytics-with-python.com\" target=\"_blank\">Derivatives Analytics @ Wiley Finance</a>\n",
    "\n",
    "**Python for Finance** |\n",
    "<a href=\"http://python-for-finance.com\" target=\"_blank\">Python for Finance @ O'Reilly</a>"
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